OpenRaiser/Intern-Atlas huggingface dataset:
Lecture video indexing using boosted margin maximizing neural networks
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system. The indexing is performed by matching high quality slide images, for which text is either known or extracted, to lower resolution video frames with possible noise, perspective distortion, and occlusions. We propose a deep neural network integrated with a boosting framework composed of two sub-networks targeting feature extraction and similarity determination to perform the matching. The trained network is given as input a pair of slide image and a candidate video frame image and produces the similarity between them. A boosting framework is integrated into our proposed network during the training process. Experimental results show that the proposed approach is much more capable of handling occlusion, spatial transformations, and other types of noises when compared with known approaches.
A survey study on major technical barriers affecting the decision to adopt cloud services
Venue: arXiv cs.SE 2017
Year: 2017

In the context of cloud computing, risks associated with underlying technologies, risks involving service models and outsourcing, and enterprise readiness have been recognized as potential barriers for the adoption. To accelerate cloud adoption, the concrete barriers negatively influencing the adoption decision need to be identified. Our study aims at understanding the impact of technical and security-related barriers on the organizational decision to adopt the cloud. We analyzed data collected through a web survey of 352 individuals working for enterprises consisting of decision makers as well as employees from other levels within an organization. The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability. The result from our logistic regression analysis confirms the criticality of the security concern, which results in an up to 26-fold increase in the non-adoption likelihood. Our study underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Automatic Recognition of Coal and Gangue based on Convolution Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

We designed a gangue sorting system,and built a convolutional neural network model based on AlexNet. Data enhancement and transfer learning are used to solve the problem which the convolution neural network has insufficient training data in the training stage. An object detection and region clipping algorithm is proposed to adjust the training image data to the optimum size. Compared with traditional neural network and SVM algorithm, this algorithm has higher recognition rate for coal and coal gangue, and provides important reference for identification and separation of coal and gangue.
Haptic Assembly and Prototyping: An Expository Review
Venue: arXiv cs.HC 2017
Year: 2017

An important application of haptic technology to digital product development is in virtual prototyping (VP), part of which deals with interactive planning, simulation, and verification of assembly-related activities, collectively called virtual assembly (VA). In spite of numerous research and development efforts over the last two decades, the industrial adoption of haptic-assisted VP/VA has been slower than expected. Putting hardware limitations aside, the main roadblocks faced in software development can be traced to the lack of effective and efficient computational models of haptic feedback. Such models must 1) accommodate the inherent geometric complexities faced when assembling objects of arbitrary shape; and 2) conform to the computation time limitation imposed by the notorious frame rate requirements---namely, 1 kHz for haptic feedback compared to the more manageable 30-60 Hz for graphic rendering. The simultaneous fulfillment of these competing objectives is far from trivial. This survey presents some of the conceptual and computational challenges and opportunities as well as promising future directions in haptic-assisted VP/VA, with a focus on haptic assembly from a geometric modeling and spatial reasoning perspective. The main focus is on revisiting definitions and classifications of different methods used to handle the constrained multibody simulation in real-time, ranging from physics-based and geometry-based to hybrid and unified approaches using a variety of auxiliary computational devices to specify, impose, and solve assembly constraints. Particular attention is given to the newly developed 'analytic methods' inherited from motion planning and protein docking that have shown great promise as an alternative paradigm to the more popular combinatorial methods.
Arbitrarily Varying Wiretap Channel with State Sequence Known or Unknown at the Receiver
Venue: arXiv cs.IT 2017
Year: 2017

The secrecy capacity problems over the general arbitrarily varying wiretap channel (AVWC), with respect to the maximal decoding error probability and strong secrecy criterion, are considered, where the channel state sequence may be known or unknown at the receiver. In the mean time, it is always assumed that the channel state sequence is known at the eavesdropper and unknown at the transmitter. Capacity results of both stochastic code (with random encoder and deterministic decoder) and random code (with random encoder and decoder) are discussed. This model includes the previous models of classic AVWC as special cases. Single-letter lower bounds on the secrecy capacities are given, which are proved to be the secrecy capacities when the main channel is less noisy than the wiretap channel. The coding scheme is based on Csiszar's almost independent coloring scheme and Ahlswede's elimination technique. Moreover, a new kind of typical sequence with respect to states is defined for this coding scheme. It is concluded that the secrecy capacity of stochastic code is identical to that of random code when the receiver knows the state sequence. Meanwhile, random code may achieve larger secrecy capacity when the state sequence is unknown by the receiver.
Analiza bezbednosnih mehanizama OSPF protokola
Venue: arXiv cs.CR 2017
Year: 2017

The security of the service and the system depends on the security of each of them components of this system. An attack on the routing protocol may be inaccurate functioning of the computer network. In some cases this is possible The attacker comes to data or artificial information for which he is not entitled. OSPF protocol is the most commonly used connection protocol. U In this paper, we conducted an OSPF security analysis and described it security mechanisms. ----- Bezbednost nekog servisa i sistema zavisi od bezbednosti svake komponente tog sistema. Napad na protokol rutiranja može proizvesti neispravno funkcionisanje računarske mreže. U pojedinim slučajevima moguće je da napadač dođe do podataka ili umetne podatke za koje nema pravo. OSPF protokol je najrasprostranjeniji protokol stanja linka (link-state protocol). U ovom radu izvršili smo analizu bezbednosti OSPF protokola i opisali njegove bezbednosne mehanizme.
Semi-Global Stereo Matching with Surface Orientation Priors
Venue: arXiv cs.CV 2017
Year: 2017

Semi-Global Matching (SGM) is a widely-used efficient stereo matching technique. It works well for textured scenes, but fails on untextured slanted surfaces due to its fronto-parallel smoothness assumption. To remedy this problem, we propose a simple extension, termed SGM-P, to utilize precomputed surface orientation priors. Such priors favor different surface slants in different 2D image regions or 3D scene regions and can be derived in various ways. In this paper we evaluate plane orientation priors derived from stereo matching at a coarser resolution and show that such priors can yield significant performance gains for difficult weakly-textured scenes. We also explore surface normal priors derived from Manhattan-world assumptions, and we analyze the potential performance gains using oracle priors derived from ground-truth data. SGM-P only adds a minor computational overhead to SGM and is an attractive alternative to more complex methods employing higher-order smoothness terms.
Kidemonas: The Silent Guardian
Venue: arXiv cs.CR 2017
Year: 2017

Advanced Persistent Threats or APTs are big challenges to the security of government organizations or industry systems. These threats may result in stealth attacks, but if the attack is confronted before the attacker end goal has been achieved, the attackers could become aggressive by changing the mode of attack or by resorting to some form of contingency plan, which might cause unexpected damage. Therefore, the attack detection and the notification to the system administrator should be done surreptitiously. This paper presents an architecture, called Kidemonas, to silently detect the threat and secretly report it to the user or the system administrator. This way the attacker is deceived into carrying out the attack, without sending any clear signal so that the defender can buy time to develop countermeasures to deal with the attack. We consider several attack scenarios and perform a security analysis to demonstrate the features of Kidemonas.
Always Lurking: Understanding and Mitigating Bias in Online Human Trafficking Detection
Venue: arXiv cs.AI 2017
Year: 2017

Web-based human trafficking activity has increased in recent years but it remains sparsely dispersed among escort advertisements and difficult to identify due to its often-latent nature. The use of intelligent systems to detect trafficking can thus have a direct impact on investigative resource allocation and decision-making, and, more broadly, help curb a widespread social problem. Trafficking detection involves assigning a normalized score to a set of escort advertisements crawled from the Web -- a higher score indicates a greater risk of trafficking-related (involuntary) activities. In this paper, we define and study the problem of trafficking detection and present a trafficking detection pipeline architecture developed over three years of research within the DARPA Memex program. Drawing on multi-institutional data, systems, and experiences collected during this time, we also conduct post hoc bias analyses and present a bias mitigation plan. Our findings show that, while automatic trafficking detection is an important application of AI for social good, it also provides cautionary lessons for deploying predictive machine learning algorithms without appropriate de-biasing. This ultimately led to integration of an interpretable solution into a search system that contains over 100 million advertisements and is used by over 200 law enforcement agencies to investigate leads.
Multivariate Cryptosystems for Secure Processing of Multidimensional Signals
Venue: arXiv cs.CR 2017
Year: 2017

Multidimensional signals like 2-D and 3-D images or videos are inherently sensitive signals which require privacy-preserving solutions when processed in untrustworthy environments, but their efficient encrypted processing is particularly challenging due to their structure, dimensionality and size. This work introduces a new cryptographic hard problem denoted m-RLWE (multivariate Ring Learning with Errors) which generalizes RLWE, and proposes several relinearization-based techniques to efficiently convert signals with different structures and dimensionalities. The proposed hard problem and the developed techniques give support to lattice cryptosystems that enable encrypted processing of multidimensional signals and efficient conversion between different structures. We show an example cryptosystem and prove that it outperforms its RLWE counterpart in terms of security against basis-reduction attacks, efficiency and cipher expansion for encrypted image processing, and we exemplify some of the proposed transformation techniques in critical and ubiquitous block-based processing applications
Joint User Scheduling and Power optimization in Full-Duplex Cells with Successive Interference Cancellation
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers a cellular system with a full-duplex base station and half-duplex users. The base station can activate one user in uplink or downlink (half-duplex mode), or two different users one in each direction simultaneously (full-duplex mode). Simultaneous transmissions in uplink and downlink causes self-interference at the base station and uplink-to-downlink interference at the downlink user. Although uplink-to-downlink interference is typically treated as noise, it is shown that successive interference decoding and cancellation (SIC mode) can lead to significant improvement in network utility, especially when user distribution is concentrated around a few hotspots. The proposed temporal fair user scheduling algorithm and corresponding power optimization utilizes full-duplex and SIC modes as well as half-duplex transmissions based on their impact on network utility. Simulation results reveal that the proposed strategy can achieve up to 95% average cell throughput improvement in typical indoor scenarios with respect to a conventional network in which the base station is half-duplex.
Deep Visual Perception for Dynamic Walking on Discrete Terrain
Venue: arXiv cs.RO 2017
Year: 2017

Dynamic bipedal walking on discrete terrain, like stepping stones, is a challenging problem requiring feedback controllers to enforce safety-critical constraints. To enforce such constraints in real-world experiments, fast and accurate perception for foothold detection and estimation is needed. In this work, a deep visual perception model is designed to accurately estimate step length of the next step, which serves as input to the feedback controller to enable vision-in-the-loop dynamic walking on discrete terrain. In particular, a custom convolutional neural network architecture is designed and trained to predict step length to the next foothold using a sampled image preview of the upcoming terrain at foot impact. The visual input is offered only at the beginning of each step and is shown to be sufficient for the job of dynamically stepping onto discrete footholds. Through extensive numerical studies, we show that the robot is able to successfully autonomously walk for over 100 steps without failure on a discrete terrain with footholds randomly positioned within a step length range of 45-85 centimeters.
SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
Venue: arXiv cs.AI 2017
Year: 2017

To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand the environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inference easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environments and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, connected modules are dependent on each other, and parameters are required to be optimized as a whole. Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it becomes harder to derive and implement those of a larger scale model. To solve these problems, in this paper, we propose a method for parameter estimation by communicating the minimal parameters between various modules while maintaining their programmatic independence. Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed.
(Gap/S)ETH Hardness of SVP
Venue: arXiv cs.CC 2017
Year: 2017

$ \newcommand{\problem}[1]{\ensuremath{\mathrm{#1}} } \newcommand{\SVP}{\problem{SVP}} \newcommand{\ensuremath}[1]{#1} $We prove the following quantitative hardness results for the Shortest Vector Problem in the $\ell_p$ norm ($\SVP_p$), where $n$ is the rank of the input lattice. $\bullet$ For "almost all" $p > p_0 \approx 2.1397$, there no $2^{n/C_p}$-time algorithm for $\SVP_p$ for some explicit constant $C_p > 0$ unless the (randomized) Strong Exponential Time Hypothesis (SETH) is false. $\bullet$ For any $p > 2$, there is no $2^{o(n)}$-time algorithm for $\SVP_p$ unless the (randomized) Gap-Exponential Time Hypothesis (Gap-ETH) is false. Furthermore, for each $p > 2$, there exists a constant $γ_p > 1$ such that the same result holds even for $γ_p$-approximate $\SVP_p$. $\bullet$ There is no $2^{o(n)}$-time algorithm for $\SVP_p$ for any $1 \leq p \leq 2$ unless either (1) (non-uniform) Gap-ETH is false; or (2) there is no family of lattices with exponential kissing number in the $\ell_2$ norm. Furthermore, for each $1 \leq p \leq 2$, there exists a constant $γ_p > 1$ such that the same result holds even for $γ_p$-approximate $\SVP_p$.
Face Translation between Images and Videos using Identity-aware CycleGAN
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement. In this problem there exist two major technical challenges: 1) designing a robust translation model between static images and dynamic videos, and 2) preserving facial identity during image-video translation. To address such two problems, we generalize the state-of-the-art image-to-image translation network (Cycle-Consistent Adversarial Networks) to the image-to-video/video-to-image translation context by exploiting a image-video translation model and an identity preservation model. In particular, we apply the state-of-the-art Wasserstein GAN technique to the setting of image-video translation for better convergence, and we meanwhile introduce a face verificator to ensure the identity. Experiments on standard image/video face datasets demonstrate the effectiveness of the proposed model in both terms of qualitative and quantitative evaluations.
Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
Venue: arXiv cs.CV 2017
Year: 2017

Images convey a broad spectrum of personal information. If such images are shared on social media platforms, this personal information is leaked which conflicts with the privacy of depicted persons. Therefore, we aim for automated approaches to redact such private information and thereby protect privacy of the individual. By conducting a user study we find that obfuscating the image regions related to the private information leads to privacy while retaining utility of the images. Moreover, by varying the size of the regions different privacy-utility trade-offs can be achieved. Our findings argue for a "redaction by segmentation" paradigm. Hence, we propose the first sizable dataset of private images "in the wild" annotated with pixel and instance level labels across a broad range of privacy classes. We present the first model for automatic redaction of diverse private information.
Transferring Autonomous Driving Knowledge on Simulated and Real Intersections
Venue: arXiv cs.LG 2017
Year: 2017

We view intersection handling on autonomous vehicles as a reinforcement learning problem, and study its behavior in a transfer learning setting. We show that a network trained on one type of intersection generally is not able to generalize to other intersections. However, a network that is pre-trained on one intersection and fine-tuned on another performs better on the new task compared to training in isolation. This network also retains knowledge of the prior task, even though some forgetting occurs. Finally, we show that the benefits of fine-tuning hold when transferring simulated intersection handling knowledge to a real autonomous vehicle.
Related families-based attribute reduction of dynamic covering information systems with variations of object sets
Venue: arXiv cs.IT 2017
Year: 2017

In practice, there are many dynamic covering decision information systems, and knowledge reduction of dynamic covering decision information systems is a significant challenge of covering-based rough sets. In this paper, we first study mechanisms of constructing attribute reducts for consistent covering decision information systems when adding objects using related families. We also employ examples to illustrate how to construct attribute reducts of consistent covering decision information systems when adding objects. Then we investigate mechanisms of constructing attribute reducts for consistent covering decision information systems when deleting objects using related families. We also employ examples to illustrate how to construct attribute reducts of consistent covering decision information systems when deleting objects. Finally, the experimental results illustrates that the related family-based methods are effective to perform attribute reduction of dynamic covering decision information systems when object sets are varying with time.
Introspection for C and its Applications to Library Robustness
Venue: arXiv cs.PL 2017
Year: 2017

Context: In C, low-level errors, such as buffer overflow and use-after-free, are a major problem, as they cause security vulnerabilities and hard-to-find bugs. C lacks automatic checks, and programmers cannot apply defensive programming techniques because objects (e.g., arrays or structs) lack run-time information about bounds, lifetime, and types. Inquiry: Current approaches to tackling low-level errors include dynamic tools, such as bounds or type checkers, that check for certain actions during program execution. If they detect an error, they typically abort execution. Although they track run-time information as part of their runtimes, they do not expose this information to programmers. Approach: We devised an introspection interface that allows C programmers to access run-time information and to query object bounds, object lifetimes, object types, and information about variadic arguments. This enables library writers to check for invalid input or program states and thus, for example, to implement custom error handling that maintains system availability and does not terminate on benign errors. As we assume that introspection is used together with a dynamic tool that implements automatic checks, errors that are not handled in the application logic continue to cause the dynamic tool to abort execution. Knowledge: Using the introspection interface, we implemented a more robust, source-compatible version of the C standard library that validates parameters to its functions. The library functions react to otherwise undefined behavior; for example, they can detect lurking flaws, handle unterminated strings, check format string arguments, and set errno when they detect benign usage errors. Grounding: Existing dynamic tools maintain run-time information that can be used to implement the introspection interface, and we demonstrate its implementation in Safe Sulong, an interpreter and dynamic bug-finding tool for C that runs on a Java Virtual Machine and can thus easily expose relevant run-time information. Importance: Using introspection in user code is a novel approach to tackling the long-standing problem of low-level errors in C. As new approaches are lowering the performance overhead of run-time information maintenance, the usage of dynamic runtimes for C could become more common, which could ultimately facilitate a more widespread implementation of such an introspection interface.